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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.21570 |
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| _version_ | 1866918107033894912 |
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| author | Shaska, Joni Mitra, Urbashi |
| author_facet | Shaska, Joni Mitra, Urbashi |
| contents | This paper proposes a novel framework for causal discovery with asymmetric error control, called Neyman-Pearson causal discovery. Despite the importance of applications where different types of edge errors may have different importance, current state-of-the-art causal discovery algorithms do not differentiate between the types of edge errors, nor provide any finite-sample guarantees on the edge errors. Hence, this framework seeks to minimize one type of error while keeping the other below a user-specified tolerance level. Using techniques from information theory, fundamental performance limits are found, characterized by the Rényi divergence, for Neyman-Pearson causal discovery. Furthermore, a causal discovery algorithm is introduced for the case of linear additive Gaussian noise models, called epsilon-CUT, that provides finite-sample guarantees on the false positive rate, while staying competitive with state-of-the-art methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21570 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Causal Link Discovery with Unequal Edge Error Tolerance Shaska, Joni Mitra, Urbashi Signal Processing This paper proposes a novel framework for causal discovery with asymmetric error control, called Neyman-Pearson causal discovery. Despite the importance of applications where different types of edge errors may have different importance, current state-of-the-art causal discovery algorithms do not differentiate between the types of edge errors, nor provide any finite-sample guarantees on the edge errors. Hence, this framework seeks to minimize one type of error while keeping the other below a user-specified tolerance level. Using techniques from information theory, fundamental performance limits are found, characterized by the Rényi divergence, for Neyman-Pearson causal discovery. Furthermore, a causal discovery algorithm is introduced for the case of linear additive Gaussian noise models, called epsilon-CUT, that provides finite-sample guarantees on the false positive rate, while staying competitive with state-of-the-art methods. |
| title | Causal Link Discovery with Unequal Edge Error Tolerance |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2507.21570 |